AI-Generated Allegations: Review Before Sending

AI-generated allegations about identifiable people require a stronger review process than ordinary drafting. A fluent description of suspected misconduct is not evidence. Before an automated system sends a report, complaint or public statement, a responsible person must check the underlying facts, the recipient and the possible harm from an incorrect claim.

NIST's AI Risk Management Framework provides a voluntary foundation for considering risks to individuals and organizations. The workflow here is an independent operational proposal for teams whose software drafts sensitive external communications. It does not assert that a particular current news allegation is true.

Identify claims about people

A system may produce allegations in customer-support replies, workplace reports, fraud alerts or summaries of complaints. The risk is not confined to a button labelled “report misconduct.” A generated email can carry the same accusation in more casual language.

Mark any workflow that connects a named person, account or organization with wrongdoing. Include indirect identification: a role, address or combination of details can identify someone even when a full name is absent.

Then distinguish observations from interpretations. “The payment log records three failed attempts” describes an observable event. “The customer is committing fraud” adds a conclusion that the log alone may not support.

Require an evidence bundle

The reviewer should see the original supporting records, not only a model summary of them. Preserve the relevant context and timestamps, while restricting access to sensitive information.

For a fictional expense-review system, the bundle might contain the submitted invoice, matching purchase-order record and the policy rule that triggered review. It should not treat the model's explanation as an additional independent source.

Use a compact structure:

Observed event:
Supporting record:
Uncertainty or missing context:
Proposed wording:
Recipient and purpose:
Reviewer decision:

The missing-context field is important. If it is blank because the system failed to consider alternatives, the review remains incomplete.

Separate drafting from delivery

Give the drafting component permission to prepare content without permission to send it externally. The send operation should receive an approved final document and a verified recipient, rather than taking an arbitrary instruction from the model.

Approval should apply to the actual text and destination. If either changes after review, require a new decision. Otherwise, an innocent draft can be approved while a later regeneration adds a stronger accusation.

For routine communications, a lightweight check may be enough. For allegations with legal, employment or safety consequences, involve people qualified to assess the issue. The purpose is to make responsibility explicit, not to ask a model to certify its own reliability.

Use wording that matches the evidence

Replace unsupported certainty with accurate descriptions. “This requires investigation” can be appropriate when the facts are incomplete. It is not a substitute for evidence, but it avoids pretending the system has established a conclusion it cannot support.

Do not invent quotes, witnesses or motivations. If the source contains a complaint, attribute it as a complaint. If two records conflict, show the disagreement rather than silently choosing the version that makes the narrative smoother.

Keep the original language available to the reviewer when a translation is involved. A mistranslated name, date or idiom can materially change an allegation.

Test for mistaken identity

Use cases with similar names, reused account labels and missing identifiers. The desired behavior is to stop or request a responsible review when identity is uncertain, rather than choosing the most plausible match.

Also test quoted accusations inside documents. A system summarizing a customer message should not convert that customer's claim into the company's own verified finding. This distinction should remain visible through every transformation and export.

Duck Cloud's text and data utilities can help compare sanitized draft versions. They cannot establish whether an allegation is true, and private evidence should remain inside the approved review environment.

Prepare a correction path

Record what was sent, to whom and under whose approval. Keep access controlled and retention purposeful. If an error is discovered, the team needs to identify the actual recipients and provide a clear correction promptly.

Practice a fictional correction scenario before launching. Can staff stop pending messages, locate delivered versions and explain the factual change? A system that sends faster than the team can correct it creates an operational problem even when most drafts are accurate.

Monitor near misses as well as delivered mistakes. Repeated reviewer corrections to names or evidence attribution may indicate a design flaw, rather than random editorial cleanup.

Give reviewers a straightforward way to reject a draft without sending it. A mandatory send-or-edit choice can encourage hurried corrections when the right decision is to hold the communication until evidence is available.

Conclusion

Sensitive communications need evidence, exact-text approval and a verified delivery path. Keep drafting separate from sending, preserve uncertainty and plan corrections. Automation can reduce clerical effort without being allowed to manufacture certainty about another person's conduct.